As generative artificial intelligence (AI) transforms how companies operate, it is also reshaping the M&A landscape—both as a driver of deal flow and as a powerful tool in deal execution. This dual impact is especially visible in Canada-US cross-border transactions, where differing legal regimes and accelerating regulation present both challenges and strategic advantages.
This insight explores how generative AI is influencing cross-border M&A, highlighting key legal considerations, diligence concerns, regulatory dynamics and value-creation opportunities.
AI as a target: Why intelligence is in demand
Generative AI systems—those that can create content, code or insights based on vast datasets—are now at the center of many acquisition strategies. According to Dentons’ 2024 Laws of AI Traction report, 70% of business leaders expect to use M&A to build AI capability over the next three years. For many, acquiring an AI-forward business is faster and more scalable than building the same capabilities in-house.
The main categories of target businesses in this space consist of:
- AI-powered SaaS tools that streamline business operations, from compliance to customer service.
- Sector-specific AI platforms, such as those focused on legal tech, health data or supply chain optimization.
- Proprietary data assets, increasingly valued as core intellectual property (IP) for training models.
Yet these targets bring unique diligence demands, both from a legal and technical perspective. While Canada and the US currently exclude copyright for AI-generated works, key differences are emerging in areas such as fair use and privacy regulation. Buyers must assess:
- Clear ownership of training data and AI-generated outputs.
- Algorithm auditability and transparency (to anticipate future regulatory obligations).
- Exposure to evolving frameworks like Canada’s proposed Artificial Intelligence and Data Act (AIDA) or US state-level regimes.
Contractual protections: Aligning AI risk and reward
In deals involving AI-driven targets, traditional transactional language is often inadequate. The complexity of AI assets requires deal terms that are tailored, forward-looking and jurisdiction-specific. In cross-border contexts, where regulatory alignment may be limited, contractual precision is key to mitigating risk and preserving deal value. Well-crafted contractual protections will allow strategic buyers to embrace innovation without absorbing disproportionate risk. Buyers and their legal advisors should consider the following in their transaction documents:
- Representations and warranties
Representations and warranties are foundational in surfacing key risks and allocating them appropriately. For AI-driven businesses, they should address:
- Ownership and provenance of training data: Sellers should confirm lawful collection and use of datasets used to train AI models.
- Ownership or valid licensing of AI models and outputs: Buyers should verify that AI models and generated outputs are owned outright or appropriately licensed.
- Compliance with applicable law: Sellers should affirm that their AI systems comply with AI, IP, privacy and data protection laws across all relevant jurisdictions.
- National security concerns: Foreign investments into Canadian or US businesses involved in critical technologies, critical infrastructure, or sensitive data, such as AI chip manufacturers, may come under regulatory scrutiny under the Investment Canada Act (ICA) or the Committee on Foreign Investment in the United States (CFIUS). Buyers may require Sellers to represent that the target does not engage in such areas and to disclose any exceptions.
- Indemnification
Given the evolving nature of AI regulation, indemnification provisions should be expanded to address:
- Infringement risks: Use of copyrighted or scraped data in training models may give rise to third-party IP claims.
- Privacy violations: Improper use of personal data, especially across borders, may trigger fines or enforcement under regimes like Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) or the California Consumer Privacy Act.
- Bias or safety concerns: Outputs from AI systems—such as discriminatory decisions or unsafe recommendations—can expose buyers to consumer claims, regulatory scrutiny or contractual disputes with customers.
Negotiating appropriate indemnity caps, baskets and survival periods becomes especially important in deals involving high-impact or public-facing AI tools.
- Covenants and post-closing obligations
Where AI is central to a target’s business model, buyers may require the seller to uphold certain operational standards after closing. Post-closing target commitments may include:
- Maintaining model governance and integrity: Ensuring the continuity of responsible AI practices, such as regular performance audits and testing. Responsible AI practices should be based on an established industry framework such as ISO/IEC 42001:2023.
- Supporting compliance with evolving laws: As AI regulation advances, buyers may require sellers to assist with system disclosures or analysis mandated under new laws.
- Enabling third-party assessments: Buyers may need ongoing access to internal documentation or audit trails to satisfy their own governance frameworks or customer demands.
- Earn-outs tied to AI performance
In many AI-focused transactions, a significant portion of enterprise value is tied to future performance rather than current revenues. Earn-outs linked to AI metrics can bridge valuation gaps and align incentives post-closing. Common triggers include:
- Accuracy, reliability or efficiency benchmarks like measurable improvements in workflow automation or predictive accuracy.
- Regulatory certifications or approvals, especially where AI tools require validation under certain industry-specific regimes.
- Customer adoption and retention, especially relating to the uptake of AI features among key clients or markets.
To avoid disputes, these metrics should be precisely defined, objectively measurable and time-bound.
AI as a deal tool: Faster and smarter M&A
While acquiring AI capabilities is one trend, using AI to execute M&A more effectively is another. Generative AI is quietly transforming how deals get done—especially in complex, cross-border settings. Key AI-driven efficiencies we are seeing in the transaction process include:
- Due diligence at speed and scale: AI platforms are now extracting key terms from contracts, flagging anomalies and organizing documents by issue type or jurisdiction, which is essential when managing multijurisdictional legal frameworks and multilingual contracts.
- Regulatory mapping: AI tools can help identify foreign investment control requirements (e.g., under the ICA or CFIUS), sector-specific compliance (e.g., health data or telecom) and ESG or sanctions risks—bringing regulatory foresight earlier into the deal lifecycle.
- Drafting and review: AI can generate early drafts of many transaction documents, such as purchase agreements, disclosure schedules and resolutions, which can be customized using legal team playbooks. It can also compare cross-jurisdictional language to ensure consistency.
- Precedent and internal knowledge retrieval: AI accelerates access to historical deal precedents and sector-specific insights.
- Multilingual capability: AI-driven translation preserves legal nuance, allowing faster turnaround times on bilingual documents—a key advantage in cross-border transactions.
Regulatory divergence: Cross-border friction points
Generative AI is subject to evolving and often inconsistent legal treatment. Key divergences between Canada and the US include:
- Privacy: Canada’s PIPEDA emphasizes consent and data localization. US privacy rules vary widely by state and federal privacy rules may govern certain sectors, such as healthcare or financial services.
- AI regulation: Canada’s AIDA proposes mandatory risk assessments and system registration. The US landscape is more fragmented—guided by White House directives and agency-level oversight as well as state laws regulating AI.
- IP and data use: US courts have been more flexible with fair use and data scraping, creating differing risk profiles for AI training practices across jurisdictions.
Companies must evaluate where AI systems are trained, where and how data is processed and which jurisdiction’s rules govern usage—especially when acquiring sensitive data assets or regulated applications. International data transfer rules may also govern AI data training.
Legal teams as strategic enablers
Despite AI’s delicate balance of benefits and risks, nearly 50% of businesses are not involving their legal teams early enough in adoption decisions, according to Dentons’ research. Yet 74% of executives say legal alignment is essential for AI success. Specialized legal advisors can add value to AI-related M&A by:
- Designing AI diligence checklists tailored by jurisdiction and industry.
- Training deal teams to spot red flags in privacy, IP and regulatory compliance.
- Aligning internal AI governance with external deal goals and obligations.
When specialized legal advisors are integrated early, companies can translate AI complexity into competitive advantage—and risk into strategy.
Conclusion: M&A for the AI era
Generative AI is no longer a novelty—it is becoming foundational to modern M&A. For companies pursuing cross-border growth, especially in the Canada-US corridor, success will depend on understanding both how AI is transforming the appeal of target entities and how it can add efficiency and enhancement to the transaction process itself.
Those who embrace this shift—backed by strategic legal counsel—won’t just be acquiring AI capability, they will be reshaping how intelligent, agile and compliant M&A gets done.
